<h1>Evaluation</h1>
<p>The problem is a multicalss classification problem. Each sample (an image) is characterized by its 4096 features. You must predict the categories of 13 classes. <br /> You are given for training a data matrix X_train of dimension 5200 samples x 1024 features and an array y_train of labels of dimension 5200 samples. You must train a model which predicts the labels for two test matrices X_valid and X_test, each having 1950 samples. <br /> There are 2 phases:</p>
<ul>
    <li><strong>Phase 1: development phase. </strong> We provide you with labeled training data and unlabeled validation and test data. Make predictions for both datasets. However, you will receive feed-back on your performance on the validation set only. The performance of your LAST submission will be displayed on the leaderboard.</li>
    <li><strong>Phase 2: final phase.</strong> You do not need to do anything. Your last submission of phase 1 will be automatically forwarded. Your performance on the test set will appear on the leaderboard when the organizers finish checking the submissions.</li>
</ul>
<p>This sample competition allows you to submit either:</p>
<ul>
    <li>Only prediction results (no code).</li>
    <li>A prediction model that must be trained and tested.</li>
</ul>
<p>The submissions are evaluated using the <strong>A</strong><strong>ccuracy</strong> metric. This metric determines de classification quality and it is computed by dividing the <strong>number of true positive</strong> (data correctly classified) by&nbsp;<strong><span class="tlid-translation translation"><span title="">total number of processed</span></span><span class="tlid-translation translation"><span title=""> data</span></span></strong><span class="tlid-translation translation"><span title="">. This kind of metric is at the same time simple and informative on the performance for this classification task g<span class="tlid-translation translation"><span title="">iven that the class distribution within this project is balanced.</span></span></span></span></p>
